Instructions to use Gherman/bert-base-NER-Russian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gherman/bert-base-NER-Russian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Gherman/bert-base-NER-Russian", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Gherman/bert-base-NER-Russian") model = AutoModelForTokenClassification.from_pretrained("Gherman/bert-base-NER-Russian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model description
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This model is a fine-tuned version of `bert-base-multilingual-cased` for Named Entity Recognition (NER) in Russian text. It can identify various entity types such as person names,
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## Intended uses & limitations
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## Model description
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This model is a fine-tuned version of `bert-base-multilingual-cased` for Named Entity Recognition (NER) in Russian text. It can identify various entity types such as person first names, middle names, last names, cities, districts, etc using the BIOLU tagging format.
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## Intended uses & limitations
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